There are many known techniques for displaying data to a user. In one example, data may be displayed in the form of a table comprising cells arranged into rows and columns, where numerical values are presented in each cell. Tables can be particularly useful when displaying data at a high level of detail. However, it can be difficult for users to establish general trends from data displayed in the form of tables. In particular, it can be difficult for users to read individual numerical values when a large number of cells are present, especially when a small display screen is used. This is especially relevant in modern environments where users tend to consume information using small display screens, such as those used for smart-phones.
In another example, data can be displayed using graphical techniques which tend to display fewer numerical values. For instance, a histogram, a bar chart, a pie chart or a line chart may be used where it is intended to illustrate general data trends, as opposed to many specific numerical values.
An example of a bar chart is illustrated in granted European patent EP 1 672 589. In a bar chart, a particular value associated with a discrete variable can be represented by the length of the bar. In this example, the length of the bar is defined relative to an axis which indicates a variable represented by the bar's length. In addition, the position of the bar is defined by another axis indicative of a discrete variable associated with the bar itself. Similarly, with pie charts the arc length of each slice in the chart is defined relative to a central axis, which is used to define the proportion of the whole that is attributed to each slice. Furthermore, the circumference of the pie chart may be considered as an axis which indicates the proportion attributed to each slice.
Each of the above-mentioned examples of data display techniques have drawbacks, when a large amount of data is displayed. Specifically, these techniques can be particularly problematic when a small display screen is used or when a large number of discrete variables or classes of data is displayed. In these cases, a user may find it difficult to recognise individual display elements. This can have serious consequences if incorrect inferences are drawn from data displayed.
In one example, a medical practitioner may wish to view the tumour sizes of a large number of different cancer patients. If the medical practitioner wishes to view the data for many different cancer patients at a particular time in order to quickly identify the most serious cases, then this would not be possible if a bar chart were used.
In another example, the medical practitioner may wish to view how the tumour sizes of the patients have varied over time. If bar chars were used for this purpose, then, the medical practitioner would be required to select many different charts for display one after another. This would be very time consuming and would require undesirable amounts of processing resources.
Thus, there exists a need for a technique that uses display resources more efficiently so that a user can visualise large quantities of data. In addition, there exists a need for a technique that allows data to be visualised in the time domain, whilst using processing resources in an efficient manner.